Variational quantum solutions to the Shortest Vector Problem

A fundamental computational problem is to find a shortest non-zero vector in Euclidean lattices, a problem known as the Shortest Vector Problem (SVP). This problem is believed to be hard even on quantum computers and thus plays a pivotal role in post-quantum cryptography. In this work we explore how (efficiently) Noisy Intermediate Scale Quantum (NISQ) devices may be used to solve SVP. Specifically, we map the problem to that of finding the ground state of a suitable Hamiltonian. In particular, (i) we establish new bounds for lattice enumeration, this allows us to obtain new bounds (resp.~estimates) for the number of qubits required per dimension for any lattices (resp.~random q-ary lattices) to solve SVP; (ii) we exclude the zero vector from the optimization space by proposing (a) a different classical optimisation loop or alternatively (b) a new mapping to the Hamiltonian. These improvements allow us to solve SVP in dimension up to 28 in a quantum emulation, significantly more than what was previously achieved, even for special cases. Finally, we extrapolate the size of NISQ devices that is required to be able to solve instances of lattices that are hard even for the best classical algorithms and find that with approximately $10^3$ noisy qubits such instances can be tackled

FireProt ASR: Automated design of ancestral proteins

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FireProt: Web server for automated design of thermostable proteins

Stable proteins are used in numerous biomedical and biotechnological applications. Unfortunately, naturally occurring proteins cannot usually withstand the harsh industrial environment, since they are mostly evolved to function at mild conditions. Therefore, there is a continuous interest in increasing protein stability to enhance their industrial potential. A number of in silico tools for the prediction of the effect of mutations on protein stability have been developed recently. However, only single-point mutations with a small effect on protein stability are typically predicted with the existing tools and have to be followed by laborious protein expression, purification, and characterization. A much higher degree of stabilization can be achieved by the construction of the multiple-point mutants. Here, we present the FireProt method [1] and the web server [2] for the automated design of multiple-point mutant proteins that combines structural and evolutionary information in its calculation core. FireProt utilizes sixteen bioinformatics tools, including several force field calculations. Highly reliable designs of the thermostable proteins are constructed by two distinct protein engineering strategies, based on the energy and evolution approaches and the multiple-point mutants are checked for the potentially antagonistic effects in the designed protein structure. Furthermore, time demands of the FireProt method are radically decreased by the utilization of the smart knowledge-based filters, protocol optimization, and effective parallelization. The server is complemented with an interactive, easy-to-use interface that allows users to directly analyze and optionally modify designed thermostable proteins. The server is freely available at http://loschmidt.chemi.muni.cz/fireprot. 1. Bednar, D., Beerens, K., Sebestova, E., Bendl, J., Khare, S., Chaloupkova, R., Prokop, Z., Brezovsky, J., Baker, D., Damborsky, J., 2015: FireProt: Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants. PLOS Computational Biology 11: e1004556. 2. Musil, M., Stourac, J., Bendl, J., Brezovsky, J., Prokop, Z., Zendulka, J., Martinek, T., Bednar, D., Damborsky, J., 2017, FireProt: Web Server for Automated Design of Thermostable Proteins, Nucleic Acids Research, in press, doi: 10.1093/nar/gkx285

Strategies and software tools for engineering protein tunnels and dynamical gates

Improvements in the catalytic activity, substrate specificity or enantioselectivity of enzymes are traditionally achieved by modification of enzymes’ active sites. We have recently proposed that the enzyme engineering endeavors should target both the active sites and the access tunnels/channels [1,2]. Using the model enzymes haloalkane dehalogenases, we have demonstrated that engineering of access tunnels provides enzymes with significantly improved catalytic properties [3] and stability [4]. User-friendly software tools Caver [5], Caver Analyst [6], CaverDock [7] and Caver Web [8], have been developed for the computational design of protein tunnels/channels; FireProt [9] and HotSpot Wizard [10] for automated design of stabilizing mutations and smart libraries. Using these tools we were able to introduce a new tunnel to a protein structure and tweak its conformational dynamics. This engineering strategy has led to improved catalytic efficiency [2], enhanced promiscuity or even a functional switch (unpublished). Our concepts and software tools are widely applicable to various enzymes with known structures and buried active sites. 1. Damborsky, J., et al., 2009: Computational Tools for Designing and Engineering Biocatalysts. Current Opinion in Chemical Biology 13: 26-34. 2. Prokop, Z., et al., 2012: Engineering of Protein Tunnels: Keyhole-lock-key Model for Catalysis by the Enzymes with Buried Active Sites. Protein Engineering Handbook, Wiley-VCH, Weinheim, pp. 421-464. 3. Brezovsky, J., et al., 2016: Engineering a de Novo Transport Tunnel. ACS Catalysis 6: 7597-7610. 4. Koudelakova, T., et al., 2013: Engineering Enzyme Stability and Resistance to an Organic Cosolvent by Modification of Residues in the Access Tunnel. Angewandte Chemie 52: 1959-1963. 5. Chovancova, E., et al., 2012: CAVER 3.0: A Tool for Analysis of Transport Pathways in Dynamic Protein Structures. PLOS Computational Biology 8: e1002708. 6. Jurcik, A., et al., 2018: CAVER Analyst 2.0: Analysis and Visualization of Channels and Tunnels in Protein Structures and Molecular Dynamics Trajectories. Bioinformatics 34: 3586-3588. 7. Vavra, O., et al., 2019: CaverDock 1.0: A New Tool for Analysis of Ligand Binding and Unbinding Based on Molecular Docking. Bioinformatics (under review). 8. Stourac, J., et al. 2019: Caver Web 1.0: Identification of Tunnels and Channels in Proteins and Analysis of Ligand Transport. Nucleic Acids Research (under review). 9. Musil, M., et al., 2017: FireProt: Web Server for Automated Design of Thermostable Proteins. Nucleic Acids Research 45: W393-W399. 10. Sumbalova, L. et al., 2018: HotSpot Wizard 3.0: Automated Design of Site-Specific Mutations and Smart Libraries in Protein Engineering. Nucleic Acids Research 46: W356-W362

QuEST-Kit/QuEST: v3.7.0

Overview This release integrates a cuQuantum backend, optimises distributed communication, and improves the unit tests. New features QuEST gained a new backend which integrates cuQuantum and Thrust for optimised simulation on modern NVIDIA GPUs. This is compiled with cmake argument -DUSE_CUQUANTUM=1, as detailed in the compile doc. Unlike QuEST's other backends, this does require prior installation of cuQuantum, outlined here. This deployment mode should run much faster than QuEST's custom GPU backend, and will soon enable multi-GPU simulation. The entirety of QuEST's API is supported! :tada: Other changes QuEST's distributed communication has been optimised when exchanging states via many maximum-size messages, thanks to the work of Jakub Adamski as per this manuscript. Functions like multiQubitUnitary() and mixMultiQubitKrausMap() have relaxed the precision of their unitarity and CPTP checks, so they will complain less about user matrices. Now, for example, a unitarity matrix U is deemed valid only if every element of U*dagger(U) has a Euclidean distance of at most REAL_EPS from its expected identity-matrix element. Unit tests now check that their initial register states are as expected before testing an operator. This ensures that some tests do not accidentally pass when they should be failing (like when run with an incorrectly specified GPU compute capability) due to an unexpected all-zero initial state. Unit tests now use an improved and numerically stable function for generating random unitaries and Kraus maps, so should trigger fewer precision errors and false test failures

Evolutionary analysis as a powerful complement to energy calculations for protein stabilization

Stability is one of the most important characteristics of proteins employed as biocatalysts, biotherapeutics, and biomaterials, and the role of computational approaches in modifying protein stability is rapidly expanding. We have recently identified stabilizing mutations in haloalkane dehalogenase DhaA using phylogenetic analysis but were not able to reproduce the effects of these mutations using force-field calculations. Here we tested four different hypotheses to explain the molecular basis of stabilization using structural, biochemical, biophysical, and computational analyses. We demonstrate that stabilization of DhaA by the mutations identified using the phylogenetic analysis is driven by both entropy and enthalpy contributions, in contrast to primarily enthalpy-driven stabilization by mutations designed by the force-field Comprehensive bioinformatics analysis revealed that more than half (53%) of 1 099 evolution-based stabilizing mutations would be evaluated as destabilizing by force-field calculations. Thermodynamic integration considers both folded and unfolded states and can describe the entropic component of stabilization, yet it is not suitable for predictive purposes due to its high computational demands. Altogether, our results strongly suggest that energetic calculations should be complemented by a phylogenetic analysis in protein-stabilization endeavors